evaluation of liquefied natural gas bunkering port selection
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Evaluation of liquefied natural gas bunkering port selection
Kim, A-R, Kwak, D-W & Seo, Y-J
Author post-print (accepted) deposited by Coventry University’s Repository Original citation & hyperlink:
Kim, A-R, Kwak, D-W & Seo, Y-J 2019, 'Evaluation of liquefied natural gas bunkering port selection', International Journal of Logistics Research and Applications, vol. (In-press), pp. (In-press) https://dx.doi.org/10.1080/13675567.2019.1642311
DOI 10.1080/13675567.2019.1642311 ISSN 1367-5567 ESSN 1469-848X Publisher: Taylor and Francis This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Logistics Research and Applications on 22/07/2019 available online: http://www.tandfonline.com/10.1080/13675567.2019.1642311 Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.
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Evaluation of liquefied natural gas bunkering port selection
A-Rom Kim
Research Institute for Gangwon, Gangeung, South Korea
Dong-Wook Kwak
Coventry Business School, Coventry University, Coventry, UK
Young-Joon Seo
School of Economics & Trade, Kyungpook National University, Daegu, South Korea
Given environment regulations on emissions from ships, shipping companies have
sought alternative fuel ships, such as LNG-powered vessels, which may give rise to
growth in liquefied natural gas (LNG) bunkering ports. Because demand for LNG-
powered vessels is expected to increase, it is worth assessing the factors that lead to the
selection of LNG bunkering ports in LNG bunkering industries. However, a lack of
academic research exists in the field of LNG bunkering. This paper employs a second-
stage empirical analysis approach that selects criteria for shipping companies’ selection
of a LNG bunkering port through a literature review and interviews, and then adopts a
fuzzy-AHP methodology to reveal the priority of the LNG bunkering port selection
criteria in LNG bunkering decision making. The results indicate that most shipping
companies decide on a LNG bunkering port with a stronger emphasis on safety/security
or port services rather than port reputation. This paper offers invaluable policy
implications for governments and port authorities that plan to build and operate LNG
bunkering ports in the near future.
Keywords: Liquefied natural gas; LNG bunkering; seaport; port selection; fuzzy-AHP
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1. Introduction
Globally, the environmental crisis caused by global warming has created the serious problem
of increasing greenhouse gases, sulfur oxides, nitrogen oxides, carbon dioxide (CO2), and
particulate matter. As a result, demand for responses to climate change and environmental
protection is increasing (Zhu, Li, and Lam 2017). This demand has strengthened environmental
regulations on air pollutant emissions from ships managed by international organizations, such
as Tier III of the International Maritime Organization (IMO) (Yang 2015). Liquefied natural
gas (LNG) is a potential solution to meeting these stronger regulations. Eyring et al. (2005)
estimated that between 1950 and 2001, CO2 emissions attributable to shipping increased more
than four-fold, a change that has significant implications for the growth of shipping-related
greenhouse gas emissions (Wen et al. 2017; Lai et al. 2013). Regarding greenhouse gas
emissions from ships, LNG is cleaner burning than heavy diesel oil (HDO) and marine gas oil
(MGO) because of its negligible sulfur content (Lloyd’s Register 2012). Zhu, Li, and Lam
(2017) noted that LNG as a fuel has the advantages of longer equipment life and lower
maintenance costs than exhausted gas scrubber and marine diesel oil (MDO)/MGO, which are
currently considered alternatives. Additionally, LNG can reduce average particulate matter
emissions by 94%.
The IMO’s regulations for a 0.5% m/m (mass/mass) sulfur cap in 2020 for marine fuels and
emission control areas (ECA) in various regions, such as the North Sea and Baltic Sea, are
factors driving the growth of LNG-powered vessels (LPV) (Kotrikla, Lilas, and Nikitakos 2017;
Lun et al. 2015). According to DNV (2012), under a high economic development and high
environmental awareness scenario, the number of LPVs has been estimated at approximately
1,000 by 2020. In particular, combined with stricter emissions control regulations, Europe and
North America are expected to experience growth in LPVs. Asia is also expected to play an
important role after 2020, driven by nations with stricter regulations, such as Singapore and
Hong Kong. Lloyd’s Register (2014) estimated that even though high sulfur fuel oil (HFO)
might remain the main fuel for shipping, the LNG quota is expected to reach 11% by 2030,
equivalent to approximately 20% of today’s bunker volume.
However, LNG bunkering infrastructure has been developed only in a handful of ports across
the globe, particularly in the North Sea and the Baltic Sea. Although ECAs exist in these
regions to comply with environmental regulations, shipping companies must operate their
vessels for their businesses. Therefore, demand for LNG bunkering is increasing naturally
given the increase in LPVs. Environmental regulations on ship emissions are causing shipping
companies to seek vessels, such as LPVs, that use alternative fuels. This change may accelerate
the growth of LNG bunkering ports. In reality, according to AG&P (2017), the global LNG
bunkering market is expected to grow from $248.64 million in 2016 to $8,187.35 million by
2023, or a compound annual growth rate of 64.7%.
Various studies show that LPV demand is expected to increase (DNV 2012, TRI-ZEN 2016,
LNG World Shipping 2016). It is worth evaluating the LNG bunker port selection factor in the
LNG bunker industry in this situation. Furthermore, a lackof academic research exists in the
field of LNG bunkering (Lloyd’s Register 2012; 2014; DNV 2012), thus presenting a plausible
reason for venturing deeper into the relatively uncharted field of LNG bunkering research. This
study employs a second-stage approach in empirical analysis and selects criteria for a shipping
company’s choice of a LNG bunkering port using literature reviews and interviews. The
approach then adopts a fuzzy-AHP methodology to reveal the priority of LNG bunkering port
selection criteria in LNG bunkering decision making. Based on this study’s results, the port
policy makers or port authorities preparing the LNG bunkering port will be able to shape or
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come up with adequate policies for more competitive LNG ports by understanding the priorities
of shipping companies when selecting LNG bunkering ports.
This paper is structured as follows. The next section reviews the relevant studies and the
third section explains the methodology. The empirical analysis is shown in the fourth section.
Finally, the fifth section provides discussion and concluding remarks.
2. Literature review
2.1 Port selection
The port selection problem is directly connected to port competitiveness. According to
Malchow and Kanafani (2004), because competition among ports has intensified, various
intermodal facilities are being improved to minimize the time that shipments are at the port. In
addition, port infrastructure and superstructure are being augmented, such as expanding storage
space and dredging channel depths to allow shipping companies to operate large vessels. Port
selection, which focuses on customer (user) decisions, is part of customer behavior research
and includes carriers/shipping companies, shippers, and freight forwarders (Brooks,
Schellinkck, and Pallis 2011; Tongzon 2009). Thus, ports should provide sound facilities and
services for users.
A considerable body of literature exists from various stakeholders, including shippers,
shipping companies, and freight forwarders, on port attractiveness, competitiveness, efficiency,
and selection using a number of methodologies. In these studies, factors that affect port
selection are identified and categorized using different classification methods. Previous
research on port choice models focused on the port choices made by shippers. Using seven
criteria, Ugboma, Ugboma, and Ogwude (2006) presented survey results to determine the
characteristics of the services that a shipper considers important when choosing a port and the
prioritization of these characteristics by importance. Other studies identified and described
various factors of shippers’ port selection using different methodologies (Tongzon 2009; Hesse
and Rodrigue 2004). Recent research has studied the choice of a port from the perspective of
liners and carriers. Lirn, Thanopoulou, and Beresford (2003) suggested a set of trans-shipment
port selection criteria from the viewpoint of the container carrier. Other studies that verified
and described the various factors in the selection of liners or carriers used several different
methodologies (Chang, Lee, and Tongzon 2008; Chou 2007).
The aforementioned literature showed that a number of common determinants exist for port
selection. Details are as follows.
<insert Table 1 around here>
2.2 LNG bunkering port
LNG bunkering requires a different process from loading of HFO and MGO given the unique
differences in the fuels’ characteristics. The most significant difference is that LNG requires
special handling relative to crude oil bunkering (ABS 2015). Generally, LNG bunkering has
three solutions: truck-to-ship (TTS), ship-to-ship (STS), and terminal-to-ship via pipeline (or
shore-based facility to ship) (TPS) (IACS 2016). Regarding TTS bunkering, the LNG truck is
connected to the ship on the quayside, generally using a flexible hose. This bunking method is
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the most widely used given relatively low investment costs and the still limited demand, in
combination with a lack of infrastructure. For these reasons, TTS bunkering is a sound,
temporary solution for LNG bunkering. In 2008, half the Norwegian coastal ferries powered
by LNG were regularly supplied by TTS bunkering, mostly overnight (Danish Maritime
Authority 2012). In STS bunkering, a small bunkering vessel is connected to the ship at
different locations (e.g., at the quayside, anchor, and sea). Given size limitations in some small
ports, only smaller bunkering vessels may be able to operate in the port area (Swedish Maritime
Technology Forum 2011). TPS bunkering is the third bunkering option in vessels moored at a
pier or a floating pier and are supplied with LNG from a storage tank through a pipeline. The
bunker station can be supplied by ship, truck, or pipeline and a liquefaction unit. For the TPS
bunkering option, LNG is transferred from a fixed storage tank on land through a cryogenic
pipeline with a flexible end piece or hose to a vessel moored to a nearby dock or pier. However,
this method is less efficient than STS bunkering because vessels must travel to the bunker
station and cannot simultaneously conduct cargo and bunkering operations (LNG Masterplan
2015).
Currently, U.S. shipping company TOTE Maritime operates 3,100 TEU-class LNG-powered
containerships. Notably, major liners, such as UASC and CMA-CGM, also recently ordered
large LNG-powered containerships. Therefore, ports may have to invest in LNG bunkering
facilities to accommodate larger LNG-powered containerships. Currently, only a few ports
have LNG bunkering facilities (e.g., Incheon, Long Beach, and Antwerp). Therefore, if a port
invests in LNG bunkering facilities today, it may have an advantage in attracting LPVs, which
will allow it to leverage the competition from other rival ports. As a representative example,
the Danish government suggested LNG as an alternative fuel for ships and environmental
improvement of state-owned vessels that use new lightweight materials and alternative fuels
(Danish Government 2012). DNV (2012) noted that 4–7 million tons of LNG p.a. is required
by 2020, which corresponds to 0.2–0.3% of global LNG production by 2010 because 1,000
more vessels will be fuelled by LNG and sailing within regions, primarily in ECAs. They
recommended that LNG bunkering be evaluated for validity, such as regarding safety, the
environment, regulations, logistics, technology, operations, finance, and the business
perspective.
<insert Figure 1 around here>
However, the literature on LNG bunkering ports is still in its initial phase. Some existing
studies explored the role of the port authority (PA) with respect to the LNG bunkering port
(Wang and Notteboom 2015), LNG bunkering port development (TRI-ZEN 2016), and a
feasibility evaluation of LNG bunkering (DNV 2012). Other studies verified and described the
various LNG bunkering factors, including a safety management system based on a formal
safety assessment (Trbojevic and Carr 2000), security risk factor table (Bajpai and Gupta 2007),
and initial framework of port security assessments (Bichou 2008). Details are as bellow.
<insert Table 2 around here>
3. Methodology
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3.1 Fuzzy-AHP
Decision makers frequently offer unclear responses rather than clear responses. Additionally,
the conversion of qualitative choices to point approximations may not be practical (Park et al.
2018). Since the fuzzy-AHP approach can take the arrogance of decision makers into
explanation, linguistic values, whose fuzzy membership functions are typically considered by
triangular fuzzy numbers, are suggested to evaluate importance ratings in preference to the
expected mathematical similarity method (Gumus 2009). Thus, when an uncertain pairwise
comparison environment exists, fuzzy-AHP should be more appropriate and satisfactory than
conformist AHP in practice (Kim and Seo 2019).
This research aims to identify the selection factors among various LNG bunkering ports
using integrated AHP techniques under a fuzzy environment. Fuzzy-AHP is used to determine
the preference weights of the evaluation using TFNs based on the various characteristics of
LNG bunkering ports (Kaya and Kahraman 2011). This research uses TFNs for the evaluation.
The steps in the fuzzy-AHP are presented as follows.
Step 1: Define scale of relative importance used in the pairwise comparison matrix
In this step, TFNs are utilized for pairwise comparisons and to find fuzzy weights because
they are intuitively easy for decision makers to use and calculate. Additionally, modelling
TFNs has proven to be effective in formulating decision problems for available information
that is subjective and imprecise. The computational process for fuzzy-AHP is detailed as
follows. A TFN can be defined by a triplet (a1, a2, a3), and the membership function 𝜇�̃�(𝑥)
is defined by:
𝜇𝐴(𝑥) = {(𝑥 − 𝑙) (𝑚 − 𝑙)⁄ , 𝑙 ≤ 𝑥 ≤ 𝑚
(𝑢 − 𝑥) (𝑢 − 𝑚)⁄ , 𝑚 ≤ 𝑥 ≤ 𝑢 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
( 1 )
This research uses nine basic linguistic terms, as in Table 1, with respect to a fuzzy nine-
level scale. Each membership function (scale of a fuzzy number) is defined by three parameters
of the symmetric TFN—the left point, the middle point, and the right point—of the range over
which the function is defined.
<insert Table 3 around here>
Step 2: Construct the fuzzy comparison matrix
In this step, pairwise comparison matrices among all criteria in the dimensions of the
hierarchy system are constructed. Linguistic terms are assigned to the pairwise comparisons by
asking which is the more important of each of the two dimensions, as in the following matrix
�̃�.
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�̃� = [
1 �̃�12
�̃�21 1⋯⋯
�̃�1𝑛
�̃�2𝑛
⋮ ⋮ ⋱ ⋮�̃�𝑛1 �̃�𝑛2 ⋯ 1
] = [
1 1/�̃�12
1/�̃�21 1
⋯⋯
�̃�1𝑛
�̃�2𝑛
⋮ ⋮ ⋱ ⋮1/�̃�𝑛1 1/�̃�𝑛2 ⋯ 1
]
( 2 )
where,
�̃�𝑖𝑗
= {9̃−1, 8̃−1, 7̃−1, 6̃−1, 5̃−1, 4̃−1, 3̃−1, 2̃−1, 1̃−1, 1̃, 2̃, 3̃, 4̃, 5̃, 6̃, 7̃, 8̃, 9̃ 1, 𝑖 ≠ 𝑗
1 𝑖 = 𝑗
Step 3: Define the fuzzy geometric mean and fuzzy weight
In this step, the geometric mean technique is used to define the fuzzy geometric mean and
fuzzy weights of each by Hsu and Huang (2014):
𝑟𝑖 = (𝑎𝑖𝑗1 × 𝑎𝑖𝑗
2 × ⋯ × 𝑎𝑖𝑗10)1/𝑛 𝑛 = 1, 2, ⋯ , 𝑛
( 3 )
𝑤𝑖 = 𝑟𝑖 × (𝑟1 + 𝑟2 + 𝑟3 + ⋯ + 𝑟𝑛)−1 ( 4 )
where aij is a fuzzy comparison value of dimension i to criterion j. Thus, ri is a geometric
mean of fuzzy comparison value of criterion i to each criterion and wi is the fuzzy weight of
the ith criterion and can be indicated by a TFN.
Step 4: Determine the best non-fuzzy performance (BNP) value
It is essential to convert the weights of each criterion into non-fuzzy values through a
procedure of defuzzification because they are still in the formula of fuzzy triangular values.
For defuzzification, the best non-fuzzy performance (BNP) value based on the center of the
area or centroid is often accepted. In this step, the BNP value for each weight (l, m, u) is
determined and given by Sun (2010).
𝐵𝑁𝑃 𝑣𝑎𝑙𝑢𝑒 =[(𝑢−𝑙)+(𝑚−𝑙)]
3+ 𝑙 ( 5 )
Step 5: Rank the criteria
The criteria are ranked using the BNP values. The criterion with a larger BNP value is
considered to have a stronger effect when compared with other criteria.
3.2 Data collection
Previous research related to the aforementioned studies was circulated among experts to
obtain better insights into the problem. In this study, through interviews and the literature
review, 20 detailed sub-criteria under five main criteria (cost, geography, port reputation, port
service, and safety/security) were identified. The overall objective of the decision process
determined for LNG bunkering port selection is on the first level of a hierarchy. The main
criteria are on the second level, and the sub-criteria are on the third level of the hierarchy (see
Table 2). The questionnaire for the analysis was distributed to each shipping company by
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referencing the world ports climate initiative (WPCI) website and the list of world LPVs (LNG
World Shipping 2016). From October 13 to November 30, 2017, 134 questionnaires were
distributed to the population and 24 were returned, for an approximate response rate of 18%.
Depending on the consistency of the answers, 20 questionnaires were finally adopted. Twenty
respondents from shipping companies consisting of CEOs, general managers, and operations
managers with professional experience answered the questionnaire. According to Abduh and
Omar (2012), one of the benefits of using AHP is that it does not require many respondents for
the analysis, and it can be applied with just one respondent. In reality, AHP has been applied
in research with small sample sizes to determine the hierarchical analysis based on experts’
opinions (Peterson, Silsbee, and Schmoldt 1994; Kamal, Al-Subhi, and Al-Harbi 2001; Shen
et al. 2015).
<insert Table 4 around here>
4. Empirical analysis
In this section, fuzzy-AHP is performed to determine the priority when shipping companies
choose a LNG bunkering port. Selecting a LNG bunkering port is important for the following
three reasons.
(1) Demand for LPVs has increased as a result of environmental regulations of international
organizations. However, scant research exists on the analysis of LNG bunkering ports.
(2) Related LNG bunkering industries are still in an early phase and technologies are new.
These industries will most likely develop and offer even more sustainable alternatives for the
future.
(3) Advanced ports (Singapore, Rotterdam) are already preparing for LNG bunkering. The
government or PA preparing the LNG bunkering port will be able to implement policies for
more competitive LNG ports by understanding the priorities of shipping companies when
selecting LNG bunkering ports.
The composition of the analysis is as follow.
<insert Figure 2 around here>
Phase 1. Identification of criteria for LNG bunkering port selection
As previously mentioned, through expert interviews and the literature review, 20 sub-criteria
for five main criteria were identified. See Table 2 for details.
Phase 2. Rank the criteria using fuzzy-AHP
Respondents were asked to construct pairwise comparisons of the five major criteria and 20
sub-criteria by employing linguistic variables. Using the arithmetic mean, the pairwise
comparison matrices of the criteria and sub-criteria are established. The results from the
computations using the pairwise comparison matrices are shown in Tables 9 and 10. The
consistency ratio values of all matrices are less than 0.1, indicating that these matrices are
sufficiently consistent. Then, linguistic expressions were transformed into FTNs and a fuzzy
evaluation matrix was established (see Table 1). The next step is to obtain a fuzzy weighted
evaluation matrix. Using the criteria weight calculated, the weighted evaluation matrix is
established by Eq. (3) and Eq. (4). The results of the analysis are shown in Tables 3–10.
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<insert Table 5-12 around here>
5. Discussion and concluding remarks
Studies on LNG bunkering port selection that incorporate applications from the perspective
of shipping companies are lacking. This paper offers invaluable policy implications for
governments and PAs that plan to build and operate LNG bunkering ports in the near future.
The results of the analysis enable them to: (1) more clearly understand the needs of shipping
companies regarding LNG bunkering ports, (2) determine how to provide efficient LNG
bunkering services, and (3) make prompt adjustments to meet their development strategies.
The results also enable LNG bunkering port managers to: (1) grasp the present strengths and
weaknesses of their ports and (2) help them establish future strategies to improve the
competitiveness of their ports. This paper represents a first step in exploring LNG bunkering
port selection.
The final ranking of the criteria is determined according to the BNP values. The results
indicate that, among the 20 sub-criteria of LNG bunkering port selection by shipping
companies, geographical location (G1) ranked first as the most competitive factor, followed by
LNG bunkering safety (SS1), port traffic (G3), and port accessibility (G2). In reality, shipping
companies normally obtain LNG bunkering services at the North Sea and the Baltic Sea given
the ECAs. In addition, in these regions, LPVs are actively being operated and LNG bunkering
ports are the most distributed. These examples might indicate that the adoption of an ECA is
highly related to the development of LNG bunkering ports. Moreover, for shipping companies
to make optimal decisions on the choice of LNG bunkering ports may be helpful. After the
analysis, the strategic recommendations may be provided to the government or PA that is
preparing the LNG bunkering port. The results indicate that most shipping companies decide
on a LNG bunkering port with a stronger emphasis on safety/security or port services rather
than port reputation. Therefore, when developing a LNG bunkering port, the government and
the PA should improve its safety and security under limited port capacity to enhance its LNG
bunker supply efficiency and shorten its waiting time. Meanwhile, the findings indicate that
the efficiency of the LNG bunkering process (PS1) was a considerable factor. Therefore, port
operators should improve the efficiency of port operations and, at the same time, secure sound
LNG bunker suppliers in the port. Such partnerships will lead more shipping companies to
obtain LNG bunker services at this port by reducing ship turnaround time (Barnes-Dabban, va
Tatenhove, and van Koppen 2017).
One of interviewees argued that “IMO's lowering of sulfur content from 3.5% to 0.5% should
eventually mean not to use fossil fuels. Therefore, considering the ship's life span is 30 years,
it is important to pay attention to what will be the regulations after 2025 and to build know-
how by trying various methods such as LNG fuel.” Another interviewee mentioned that "The
desirable direction of LNG-powered vessels is to promote shipbuilding orders, and it is
necessary to apply more than mid-sized public shipbuilding orders as LNG-propelled vessels.
Therefore, it is necessary to gradually expand the infrastructure facilities that can supply LNG
as ship fuel, and to support cooperation between international ports between the governments."
The Korean government is organizing the 'LNG Propulsion Shipbuilding Industry
Association' in cooperation with shipbuilding, shipping, and port, and is conducting a pilot
project. The Korean government of is supporting POSCO's pilot project to introduce the
180,000-ton LPV. Currently, eight organizations are cooperating, including the Ministry of
Maritime Affairs and Fisheries, the Ministry of Industry and Commerce, POSCO, the Korea
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Gas Corporation, the Korean Register, the Korea Development Bank, the LNG Bunkering
Industry Association and the Shipbuilding, and Marine Plant Research Institute (Chosun Biz
2017).
In light of these interviews and government policies, we recommend the following policies,
taking into account both the earlier theoretical assessments and empirical evidence.•
Incentive/discount policy: The incentive and discount policy is a significant problem arising
from the introduction of LPVs. The government and the PA should use various forms of
financial support to promote LNG bunkering in its port, such as by developing a differential
port tariff for LPVs, such as the Green Award at the port of Rotterdam and the Environmental
Ship Index (ESI) of WPCI.
• Communication policy: The government and the PA should take a conative coordinating
role with respect to sustaining honourable communication within the port community regarding
the its LNG bunkering port, such as by (a) improving public opinion to promote the use of
LNG bunkering ports and (b) projecting a publicity campaign or by forming conferences,
seminars, or workshops (Lun 2011).
• Collaborative policy: The government or the PA preparing a LNG bunkering port should
institute collaboration opportunities with stakeholders of the port (e.g., industrial players such
as LNG bunkering ports, LNG suppliers, and port users). Collaboration can focus on the
development of the LNG port (e.g., location selection), the safety assessment of the LNG port
environment, and the development of bunkering standards and guidelines. Collaboration is
believed to improve interactive knowledge and its sharing, which can reduce market
uncertainty (Wang and Notteboom 2015).
The following items highlight the study’s limitations: (1) because no major LNG bunkering
ports exist, shipping companies have limited selection; (2) for the same reason, this study
cannot propose alternative LNG bunkering ports; and (3) shipping companies’ decisions may
be non-objective and may neglect actual vessel and port conditions. Therefore, important
additional studies can follow this paper. Future research directions are as follows: (1) shipping
companies that lack LPVs should be added to the analysis; (2) after large LNG bunkering ports
are constructed, alternatives should be analyzed; and (3) a two-phase methodology combining
fuzzy-AHP-TOPSIS and a sensitivity analysis can be incorporated to confirm the robustness
of the analysis. Finally, a stated preference approach could have been selected and
governments/PA should have been interviewed or have data collected from them in terms of
how they would like to further develop LNG bunkering facilities in details. Nevertheless, the
authors strongly believe that the study has provided an ideal platform for further research on
this increasingly important subject.
References
Abduh M. and M.A. Omar. 2012. “Islamic-bank selection criteria in Malaysia: An AHP
approach.” Business Intelligence Journal 5 (2): 281–281.
AG&P. 2017. Global LNG landscape. http://www.agp.ph/pdf/rs-
brochure/Global_LNG_landscape.pdf. Accessed 13 Sep 2017.
10
Acosta M., D. Coronado, and M.D.M. Cerban. 2011. “Bunkering competition and
competitiveness at the ports of the Gibraltar Strait.” Journal of Transport Geography 19:
911–916.
American Bureau of Shipping (ABS). 2014. Bunkering of Liquefied Natural Gas-fueled
Marine Vessels in North America.
http://www.lngbunkering.org/sites/default/files/2014,%20ABS,%20Bunkering%20of%2
0Liquefied%20Natural%20Gas-
fueled%20Marine%20Vessels%20in%20North%20America_0.pdf. Accessed 15 Sep
2017.
American Bureau of Shipping (ABS). 2015. LNG Bunkering: Technical and Operational
Advisory. https://ww2.eagle.org/content/dam/eagle/advisories-and-
debriefs/ABS_LNG_Bunkering_Advisory.pdf. Accessed 13 Sep 2017.
Bajpai S. and J.P. Gupta. 2007. “Securing oil and gas infrastructure.” Journal of Petroleum
Science and Engineering 55 (1-2): 174–186.
Barnes-Dabban H., J.P.M. van Tatenhove, and K.C.S.A. van Koppen. 2017. “Termeer
K.J.A.M., Institutionalizing environmental reform with sense-making: West and Central
Africa ports and the “green port” phenomenon.” Marine Policy 86: 111–120.
Bichou K. 2008. Security and risk-based models in shipping and ports: Review and critical
analysis. Discussion Paper, London, UK: OECD/ITF Joint Transport Research Centre.
Bird J. 1988. “Freight forwarders speak: The perception of route competition via seaports in
the European communities Research project—Part II.” Maritime Policy and Management
15 (2): 107–105.
Brooks M.R., T. Schellinck, and A.A. Pallis. 2011. “A systematic approach for evaluating port
effectiveness.” Maritime Policy & Management 8 (3): 315–334.
Chang Y.T., S.Y. Lee, and J.L. Tongzon. 2008. “Port selection factors by shipping lines:
Different perspectives between trunk liners and feeder service providers.” Marine Policy
32 (6): 877–885.
Chou C.C. 2007. “A fuzzy MCDM method for solving marine transshipment container port
selection problems.” Applied Mathematics and Computation 186 (1): 435–444.
Chosun Biz. 2017. [Ship environmental regulation] ③ 'Strongest in history' SOx emission
regulation ... Shipping industry worries. http://biz.chosun.com/site/data/html_dir/2017/10/31/2017103102341.html?Dep0=twitter. Accessed 29 Apr 2019.
Danish Government. 2012. Denmark at Work: Plan for Growth in the Blue Denmark.
https://www.dma.dk/Vaekst/MaritimErhvervspolitik/Documents/denmark%20at%20wor
k%20-%20plan%20for%20growth%20in%20the%20blue%20denmark.pdf. Accessed 14
Sep 2017.
Danish Maritime Authority. 2012. North European LNG Infrastructure Project: A feasibility
study for an LNG filling station infrastructure and test of recommendations.
http://www.lngbunkering.org/sites/default/files/2012%20DMA%20North%20European
%20LNG%20Infrastructure%20Project_0.pdf. Accessed 14 Sep 2017.
DNV. 2012. LNG bunkering demand and bunkering infrastructure.
http://www.shippingtech.it/presentazioniPST2012/GreenShippingSummit/GiacomoMea
zzi.pdf. Accessed 13 Sep 2017.
Eyring V., H.W. Köhler, J. van Aardenne, and A. Lauer. 2005. “Emissions from international
shipping: 1, last 50 years.” Journal of Geophysical Research 110 (D17305: 1–12.
11
Gohomene D.A., S. Bonsal, and E, Maistralis. 2016. “The attractiveness of ports in West
Africa: Some lessons from shipping lines’ port selection.” Growth and Change 47 (3):
416–426.
Gumus A.T. 2009. “Evaluation of Hazardous Waste Transportation Firms by using a Two-step
Fuzzy AHP and TOPSIS Methodology.” Expert Systems with Applications 36 (2-2):
4067–4074.
Hesse M. and J.P Rodrigue. 2004. “The transport geography of logistics and freight
distribution.” Journal of Transport Geography 12 (3): 171–184.
Hsu, W.-K. and S.-H. Huang. 2014. “Evaluating the service requirements of Taiwanese
international port distribution centres using IPA model based on fuzzy AHP.” International Journal of Shipping and Transport Logistics 6 (6): 632-651.
IACS. 2016. LNG bunkering guidelines. https://www.infomarine.gr/rules-regulations/iacs-
lng-bunkering-guidelines-2016-08.html. Accessed 13 Sep 2017.
Kamal M., Al-Subhi, and Al-Harbi. 2001. “Application of the AHP in project management.” International Journal of Project Management 19 (1): 19–27.
Kaya T. and C. Kahraman. 2011. “An integrated fuzzy AHP–ELECTRE methodology for
environmental impact assessment.” Expert Systems with Applications 38 (7): 8553–8562.
Kim, A.-R., Seo, Y.-J. 2019. The reduction of SOx emissions in the shipping industry: The
case of Korean companies, Marine Policy 100, 98-106.
Kotrikla A.M., T. Lilas, and N. Nikitakos. 2017. “Abatement of air pollution at an Aegean
island port utilizing shore side electricity and renewable energy.” Marine Policy 75: 238–248.
Kuznetsov A., J. Dinwoodie, D. Gibbs, M. Sansom, and H. Knowles. 2015. “Towards a
sustainability management system for smaller ports.” Marine Policy 54: 59–68.
Lai, K.-H., Y.H.V. Lun, C.W.Y. Wong, and T.C.E. Cheng. 2013. “Measures for evaluating
green shipping practices implementation.” International Journal of Shipping and
Transport Logistics 5 (2): 217-235.
Lirn T.C., H.A. Thanopoulou, and A.K.C. Beresford. 2003. “Transhipment port selection and
decision-making behaviour: Analysing the Taiwanese case.” International Journal of
Logistics Research and Applications 6 (4): 229–244.
Lirn T.C., H.A. Thanopoulou, M.J. Beynon, and A.K.C. Beresford. 2004. “An application of
AHP on transhipment port selection: A global perspective.” Maritime Economics &
Logistics 6: 70–91.
Lloyd’s Register. 2012. Lloyd’s Register LNG Bunkering Infrastructure Study.
http://old.mareforum.com/SHIPFINANCEPRESENTATIONS2012/POULOVASSILIS
_Apostolos.pdf. Accessed 13 Sep 2017.
Lloyd’s Register. 2014. LNG Bunkering Infrastructure Survey 2014.
http://www.lngbunkering.org/sites/default/files/2014%20Lloyds%20Register%20LNG%
20Bunkering%20Infrastructure%20Survey.pdf. Accessed 13 Sep 2017.
LNG Masterplan. 2015. LNG Bunkering. Regulatory framework and LNG bunker procedures.
http://www.lngmasterplan.eu/pilots/overview. Accessed 14 Sep 2017.
LNG World Shipping. 2016. Revealed – the global LNG-fuelled fleet.
http://www.lngworldshipping.com/news/view,revealed-the-global-lngfuelled-
fleet_42508.htm. Accessed 14 Sep 2017.
Lun, Y.H.V. 2011. “Green management practices and firm performance: A case of container
terminal operations.” Resources, Conservation and Recycling 55 (6): 559-566.
12
Lun, Y.H.V., K-H. Lai, C.W.Y. Wong, and T.C.E. Cheng. 2015, “Environmental governance
mechanisms in shipping firms and their environmental performance,” Transportation
Research Part E 78: 82-92.
Malchow M., A. Kanafani. 2004. “A disaggregate analysis of port selection.” Transportation
Research Part E 40 (4): 317–337.
Ng K.Y. 2006. “Assessing the attractiveness of ports in the North European Container
Transhipment Market: An agenda for future research in port competition.” Maritime
Economics & Logistics 8 (3): 234–250.
Nir A., K. Lin, and G. Liang. 2003. “Port choice behavior: From the perspective of the shipper.” Maritime Policy and Management 30 (2): 165–73.
Notteboom T.E. and B. Vernimmen. 2009. “The effect of high fuel costs on liner service
configuration in container shipping.” Journal of Transport Geography 17: 325–337.
Peterson D.L., D.G. Silsbee, and D.L. Schmoldt. 1994. “A case study of resources management
planning with multiple objectives and projects.” Environmental Management 18 (5): 729–742.
Port of Antwerp. 2015. LNG bunkering in the Port of Antwerp.
http://www.intertanko.com/Documents/ISTEC%20LNG%20WG%202015/P%20of%20
A%20-%20LNG%20Bunkering.pdf. Accessed 15 Sep 2017.
Port of Gothenburg. 2017. LNG Operating Regulations Including LNG Bunkering.
https://www.google.co.kr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ua
ct=8&ved=0ahUKEwjyy_HG98zYAhVHUbwKHcL0CrUQFgglMAA&url=https%3A
%2F%2Fwww.goteborgshamn.se%2FFileDownload%2F%3FcontentReferenceID%3D1
0342&usg=AOvVaw1TIarN0bPPXHSaD6QkmY1A. Accessed 15 Sep 2017.
Saeed N. 2009. “An analysis of carriers’ selection criteria when choosing container terminals
in Pakistan.” Maritime Economic and Logistics 11: 270–288.
Shen, L., K. Mathiyazhagan, D. Kannan, and W. Ying. 2015. “Study on analysing the criteria's
for selection of shipping carriers in Chinese shipping market using analytical hierarchy
process.” International Journal of Shipping and Transport Logistics 7 (6): 742-757.
SSPA. 2017. Safety manual on LNG bunkering procedures for the Port of Helsinki, SSPA
Report.
http://www.portofhelsinki.fi/sites/default/files/attachments/Port%20of%20Helsinki_%20
Safety%20manual%20on%20LNG%20bunkering.pdf. Accessed 15 Sep 2017.
Sun S.-C. 2010. “A performance evaluation model by integrating fuzzy AHP and fuzzy
TOPSIS methods.” Expert Systems with Applications 37: 7745–7754.
Swedish Maritime Technology Forum. 2011. LNG ship to ship bunkering procedure.
http://www.lngbunkering.org/sites/default/files/SMTF,%20Ship%20to%20Ship%20Bun
kering.pdf. Accessed 14 Sep 2017.
Tetraplan. 2014. LNG in shipping – Perspectives for regional ports.
http://archive.northsearegion.eu/files/repository/20150220181203_Hanstholm-LNG-
regional-ports.pdf. Accessed 14 Sep 2017.
Tiwari P., H. Itoh, and H. Doi. 2003. “Shippers’ port and carrier selection behaviour in China:
A discrete choice analysis.” Maritime Economics and Logistics 5: 23–39.
Tongzon J.L. 2009. “Port choice and freight forwarders.” Transportation Research Part E 45:
186–195.
Trbojevic V.M. and B.J. Carr. 2000. “Risk-based methodology for safety improvement in
ports.” Journal of Hazardous Materials 71 (1-3): 467–480.
13
TRI-ZEN. 2016. LNG Market Perspective. http://www.onthemosway.eu/wp-
content/uploads/2016/02/LNG-Bunkers-Perspective-Feb-2016-Foggy-Passage.pdf.
Accessed 14 Sep 2017.
Ugboma C. O. Ugboma, and I.C. Ogwude. 2006. “An analytic hierarchy process (AHP)
approach to port selection decisions – empirical evidence from Nigerian ports.” Maritime
Economics & Logistics 8 (3): 251–266.
Wang S. and Q. Meng. 2012. “Liner ship fleet deployment with container transshipment
operations.” Transportation Research Part E 48 (2): 470–484.
Wang S. and T. Notteboom. 2015. “The role of port authorities in the development of LNG
bunkering facilities in North European ports.” WMU Journal of Maritime Affairs 14: 61–92.
Wang Y., G.-T. Yeo, and A.K.Y. Ng. 2014. “Choosing optimal bunkering ports for liner
shipping companies: A hybrid Fuzzy-Delphi-TOPSIS approach.” Transport Policy 35:
358–365.
Wen, Y., X. Geng, L. Wu, T.L. Yip, L. Huang, and D. Wu. 2017. “Green routing design in
short seas.” International Journal of Shipping and Transport Logistics 9 (3): 371-390.
World Ports Climate Initiative. from http://www.lngbunkering.org/lng/map/node. Accessed 14
Sep 2017.
Yang, Y.-C. 2015. “Determinants of container terminal operation from a green port
perspective.” International Journal of Shipping and Transport Logistics 7 (3): 319-346.
Zhu M., K.X. Li, and J.S.L. Lam. 2017. “Incentive policy for reduction of emission from ships:
A case study of China.” Marine Policy 86: 253–258.
14
Figure 1. LNG bunkering possible and planed ports in the world (Source: WPCI website)
15
Figure 2. The methodology of two main stages
Establish decision group Literature review
Identify and finalized the criteria to be used in
evaluation
Structuring decision hierarchy
Approve decision
hierarchy?
No
Phase 1
Defining scale of relative importance used in pairwise comparison matrix
Yes
Construct the fuzzy comparison matrix
Check consistency ration (CR)
CR≤0.1 No
Necessary modification
Calculate the weight of criteria’s
Yes
Phase 2: fuzzy
TFN
𝐶𝑅 = 𝐶𝐼𝑅𝐼ൗ
Rank the criteria (Rank solutions of LNG bunkering port selection)
According to the BNP
values in high order
BNP value= [(u-l) + (m-l)]
/ 3+l
16
Table 1. Literature on the main factors that determine port selection
Factors References
Infrastructure
Ugboma, Ugboma, and Ogwude 2006; Tiwari, Itoh, and Doi
2003
Geographic location Chang, Lee, and Tongzon 2008; Saeed 2009
Costs Ugboma, Ugboma, and Ogwude 2006; Saeed 2009
Quality of port
services Tongzon 2009; Lirn et al. 2004
17
Table 2. Literature on the main factors that determine LNG bunkering port
Factors References
LNG bunkering safety DNV 2012; ABS 2015; IACS 2016
Price of LNG fuel
Lloyd’s Register 2012; 2014; ABS 2015; Tetraplan
2014
Port location Lloyd’s Register 2012; 2014; Tetraplan 2014
Technologies for LNG
bunkering DNV 2012; ABS 2015
18
Table 3. Membership function of linguistic scale
Fuzzy number Linguistic term Scale of fuzzy number
1 Equal (1, 1, 1)
2 Weak advantage (1, 2, 3)
3 Not bad (2, 3, 4)
4 Preferable (3, 4, 5)
5 Good (4, 5, 6)
6 Fairly good (5, 6, 7)
7 Very good (6, 7, 8)
8 Absolute (7, 8, 9)
9 Perfect (8, 9, 10)
19
Table 4. The criteria and their descriptions
Main
criteria Sub-criteria Description
Cost (M1)
Incentive/discount
(C1)
Discounts and incentives on eco-friendly vessels from
environmental regulations of international organizations (Danish
Maritime Authority 2012; Wang and Notteboom 2015; Port of
Gothenburg 2017)
LNG price (C2) Low LNG price (Register 2012; ABS 2015; Wang and
Notteboom 2015; Tetraplan 2014)
Port service charge
(C3) Low port service charge (Tongzon 2009; Hesse and Rodrigue,
2004; Gohomene, Bonsal, and Maistralis 2016)
Ship turnaround
time (C4) Short ship turnaround time attributable to saving operating costs
(Saeed, 2009; Nir, Lin, and Liang 2003; Bird 1988)
Geography
(M2)
Geographical
location (G1) Proximity to ECAs or main navigation routes (Tetraplan 2014;
Ng 2006; SSPA 2017)
Port accessibility
(G2) Closeness to shipping companies’ service routes (Acosta,
Coronado, and Cerban 2011; Wang, Yeo, and Ng 2014)
Port traffic (G3) Number of calls at port (Tiwari, Itoh, and Doi 2003; Lirn,
Thanopoulou, and Beresford 2003; Tetraplan, 2014)
Port weather
conditions (G4) Sound weather conditions for LNG bunkering (Notteboom and
Vernimmen 2009)
Port
reputation
(M3)
Experienced
human resources
(PR1)
Education and training requirement for workers of the bunker
vessel and experienced workers at the port (Lloyd’s Register 2014;
Danish Maritime Authority 2012; SSPA 2017)
Port disputes (PR2) Low level of port disputes (Acosta, Coronado, and Cerban 2011)
Public
opinion/word of
mouth (PR3)
Retain/develop positive public perception of the port (Tongzo,
2009; Ugboma, Ugboma, and Ogwude 2006; Ng, 2006)
Technical
conditions related to
LNG bunkering (PR4)
Required facilities and technology for LNG bunkering (e.g., TTS:
Truck-to-ship, STS: Ship-to-ship, TPS: Terminal-to-ship) (DNV
2012; ABS 2015; ASCS 2016; Danish Maritime Authority 2012)
Port
service (M4)
Efficiency
of LNG bunkering
process (PS1)
Short bunkering time attributable to the efficiency of the LNG
bunkering process (ASCS 2016; Wang and Notteboom 2015; Port
of Gothenburg, 2017)
Infrastructure
/superstructure
(PS2)
Infrastructure and facilities provision for LNG bunkering (Port of
Gothenburg 2017; ABS 2014; Port of Antwerp 2015; Wang and
Meng 2012)
Port congestion
(PS3) Low port congestion (Lloyd’s Register 2012; 2014; Tetraplan
2014)
Relationship among
stakeholders (PS4) Sound relationship among LNG ports, LNG suppliers, and port
users (Chang, Lee, and Tongzon 2008; Saeed, 2009; Ng, 2006)
Safety
/security
(M5)
LNG bunkering
safety (SS1)
LNG bunkering safety (e.g., ESD; Emergency Shut-down
Systems, Safeguard Systems) (Lirn et al. 2004; IACS 2016; ABS
2015; Trbojevic and Carr 2000)
Port security (SS2) Number of accidents, accidents prevented, and guards (Bichou
2008; Bajpai and Gupta 2007; SSPA 2017)
20
LNG bunkering
regulations (SS3)
Compliance with regulations and standards for LNG bunkering,
such as regulations of SIGTTO (Society of International Gas
Tankers and Terminal Operators), OCIMF (Oil Companies
International Marine Forum), IMO (International Maritime
Organization), ISO (International Organization for
Standardization), CEN (European Committee for Standardization),
and NFPA (National Fire Protection Association) (Lloyd’s Register
2012; DNV 2012; ABS 2014; Port of Gothenburg 2017; SSPA
2017)
LNG supply
regulations (SS4)
Compliance with regulations and standards for LNG supply, such
as ISO.TS 18683:2015 Guidelines for Systems and Installations for
Supply of LNG as Fuel to Ships, LNG Bunker Checklists developed
by IAPH’s (International Association of Ports and Harbors) WPCI
(World Ports Climate Initiative) (Lloyd’s Register 2014; ABS 2015;
Danish Maritime Authority 2012; Wang and Notteboom 2015; Ng
2006)
21
Table 5. Fuzzy comparison matrix of the major criterion
M1 M2 M3 M4 M5
M1 (1, 1, 1) (0.523, 0.543, 0.566) (1.431, 1.550, 1.650) (0.699, 0.780, 0.933) (0.552, 0.577, 0.608)
M2 (1.765, 1.841, 1.911) (1, 1, 1) (1.813, 1.885, 1.951) (1.578, 1.676, 1.762) (1.116, 1.231, 1.311)
M3 (0.606, 0.645, 0.699) (0.512, 0.530, 0.552) (1, 1, 1) (0.550, 0.575, 0.606) (0.506, 0.523, 0.543)
M4 (1.072, 1.282, 1.431) (0.568, 0.597, 0.634) (1.650, 1.738, 1.817) (1, 1, 1) (0.608, 0.649, 0.707)
M5 (1.644, 1.733, 1.813) (0.763, 0.812, 0.896) (1.841, 1.911, 1.974) (1.414, 1.540, 1.644) (1, 1, 1)
22
Table 6. Fuzzy comparison matrix of the cost criterion
C1 C2 C3 C4
C1 (1, 1, 1) (1.234, 1.390, 1.516) (1.072, 1.282, 1.431) (1.149, 1.246, 1.320)
C2 (0.660, 0.719, 0.812) (1, 1, 1) (0.660, 0.719, 0.812) (1.072, 1.116, 1.149)
C3 (0.699, 0.780, 0.933) (1.231, 1.390, 1.516) (1, 1, 1) (1.116, 1.231, 1.311)
C4 (0.758, 0.803, 0.871) (0.871, 0.896, 0.933) (0.763, 0.812, 0.896) (1, 1, 1)
23
Table 7. Fuzzy comparison matrix of the geography criterion
G1 G2 G3 G4
G1 (1, 1, 1) (1.718, 1.801, 1.876) (1.789, 1.863, 1.931) (1.841, 1.911, 1.974)
G2 (0.533, 0.555, 0.582) (1, 1, 1) (0.836, 0.933, 0.072) (1.374, 1.506, 1.614)
G3 (0.518, 0.537, 0.559) (0.933, 1.072, 1.196) (1, 1, 1) (1.231, 1.105, 1.534)
G4 (0.506, 0.523, 0.543) (0.620, 0.664, 0.728) (0.652, 0.712, 0.812) (1, 1, 1)
24
Table 8. Fuzzy comparison matrix of the port reputation criterion.
PR1 PR2 PR3 PR4
PR1 (1, 1, 1) (1.707, 1.789, 1.863) (1.733, 1.813, 1.885) (1.149, 1.335, 1.473)
PR2 (0.37, 0.559, 0.586) (1, 1, 1) (1.320, 1.463, 1.578) (0.568, 0.610, 0.634)
PR3 (0.530, 0.552, 0.577) (0.634, 0.683, 0.758) (1, 1, 1) (0.547, 0.584, 0.602)
PR4 (0.679, 0.749, 0.871) (1.578, 1.639, 1.762) (0.661, 1.712, 1.829) (1, 1, 1)
25
Table 9. Fuzzy comparison matrix of the port service criterion.
PS1 PS2 PS3 PS4
PS1 (1, 1, 1) (1.231, 1.390, 1.516) (1.639, 1.730, 1.810) (0.777, 0.846, 0.922)
PS2 (0.660, 0.719, 0.812) (1, 1, 1) (1.414, 1.540, 1.644) (0.634, 0.683, 0.758)
PS3 (0.552, 0.578, 0.610) (0.608, 0.649, 0.707) (1, 1, 1) (0.512, 0.530, 0.550)
PS4 (1.084, 1.182, 1.096) (1.320, 1.463, 1.578) (1.817, 1.888, 1.954) (1, 1, 1)
26
Table 10. Fuzzy comparison matrix of the safety/security criterion.
SS1 SS2 SS3 SS4
SS1 (1, 1, 1) (1.453, 1.578, 1.681) (1.603, 1.699, 1.783) (1.614, 1.712, 1.796)
SS2 (0.595, 0.634, 0.688) (1, 1, 1) (0.912, 1.041, 1.149) (0.922, 1.023, 1.196)
SS3 (0.561, 0.589, 0.624) (0.871, 0.960, 1.096) (1, 1, 1) (1.010, 1.041, 1.088)
SS4 (0.557, 0.584, 0.692) (0.836, 0.978, 1.084) (0.919, 0.960, 1.001) (1, 1, 1)
27
Table 11. Ranking of main criteria for LNG bunkering selection.
Major criterion Consistency Major
criterion weight
Major criterion
BNP Ranking
Cost (M1)
0.091
(0.142, 0.157, 0.177) 0.159 4
Geography (M2) (0.257, 0.283, 0.309) 0.283 1
Port reputation (M3) (0.111, 0.121, 0.133) 0.122 5
Port service (M4) (0.165, 0.185, 0.207) 0.186 3
Safety/security (M5) (0.230, 0.254, 0.280) 0.255 2
28
Table 12. Ranking of sub-criteria and total ranking for LNG bunkering selection
Sub-
criterion Consistency
Local Normalized
Sub-criterion
weight BNP Ranking BNP Ranking
C1
0.066
(0.257, 0.302, 0.345) 0.301 1 0.048 11
C2 (0.191, 0.216, 0.248) 0.218 4 0.035 17
C3 (0.229, 0.266, 0.310) 0.268 2 0.043 12
C4 (0.195, 0.216, 0.245) 0.219 3 0.035 16
G1
0.088
(0.351, 0.380, 0.411) 0.381 1 0.108 1
G2 (0.201, 0.227, 0.252) 0.226 3 0.064 4
G3 (0.200, 0.228, 0.253) 0.226 2 0.064 3
G4 (0.153, 0.169, 0.189) 0.170 4 0.048 10
PR1
0.094
(0.309, 0.346, 0.380) 0.345 1 0.042 14
PR2 (0.181, 0.201, 0.221) 0.201 3 0.025 19
PR3 (0.149, 0.164, 0.181) 0.165 4 0.020 20
PR4 (0.263, 0.289, 0.327) 0.293 2 0.036 15
PS1
0.096
(0.259, 0.289, 0.323) 0.290 2 0.054 9
PS2 (0.203, 0.225, 0.257) 0.228 3 0.042 13
PS3 (0.149, 0.161, 0.179) 0.163 4 0.030 18
PS4 (0.294, 0.325, 0.347) 0.322 1 0.060 5
SS1
0.090
(0.319, 0.356, 0.393) 0.356 1 0.091 2
SS2 (0.193, 0.221, 0.254) 0.223 4 0.057 6
SS3 (0.191, 0.213, 0.239) 0.215 3 0.055 7
SS4 (0.185, 0.210, 0.240) 0.212 2 0.054 8
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